Celiac disease (CD) is developed after gluten ingestion in genetically susceptible individuals. It can appear at any time in life, but some differences are commonly observed between individuals with onset early in life or in adulthood. We aimed to investigate the molecular basis underlying those differences. We collected 19 duodenal biopsies of children and adults with CD and compared the expression of 38 selected genes between each other and with the observed in 13 non-CD controls matched by age. A Bayesian methodology was used to analyze the differences of gene expression between groups. We found seven genes with a similarly altered expression in children and adults with CD when compared to controls (C2orf74, CCR6, FASLG, JAK2, IL23A, TAGAP and UBE2L3). Differences were observed in 13 genes: six genes being altered only in adults (IL1RL1, CD28, STAT3, TMEM187, VAMP3 and ZFP36L1) and two only in children (TNFSF18 and ICOSLG); and four genes showing a significantly higher alteration in adults (CCR4, IL6, IL18RAP and PLEK) and one in children (C1orf106). This is the first extensive study comparing gene expression in children and adults with CD. Differences in the expression level of several genes were found between groups, being notorious the higher alteration observed in adults. Further research is needed to evaluate the possible genetic influence underlying these changes and the specific functional consequences of the reported differences.

Autoimmune diseases like celiac disease (CeD) and ulcerative colitis (UC) show a common genetic background defined by the existence of shared susceptibility loci. We aimed to go deeper into this common genetic background through performing a cross-disease study based on gene expression. We measured the expression of 21 genes located in 13 CeD-UC susceptibility regions, and 10 genes in five CeD risk regions. Determinations were carried out in colon/rectum samples from 13 UC patients (inflamed and uninflamed tissue) and four colon samples from controls. Duodenal samples from 19 CeD patients and 12 controls were used for comparisons. Differences were analyzed using the Bayesian method. The shared chromosomal regions containing TNFAIP3, PTPN2, ICOSLG, C1orf106, and IL21 showed similar results in both diseases. FASLG, PLEK, CCR4, and TAGAP, all located in CeD risk loci, were up-regulated in both CeD and UC patients. Finally, ZFP36L1, ZMIZ1, PUS10, UBE2L3, and BACH2 showed opposite results in CeD and UC. A high complexity underlies autoimmune common susceptibility loci, as the expression pattern of the studied genes does not always correlate with the one expected attending to the apparent genetic background. Differentially expressed genes such as ZFP36L1, ZMIZ1, PUS10, and BACH2 deserve further research in autoimmune diseases. K E Y W O R D Sassociation signals, autoimmune diseases, candidate genes, disease susceptibility, gene expression 86

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We developed two models for real-time monitoring and forecasting of the evolution of the COVID-19 pandemic: a non-linear regression model and an error correction model. Our strategy allows us to detect pandemic peaks and make short- and long-term forecasts of the number of infected, deaths and people requiring hospitalization and intensive care. The non-linear regression model is implemented in an expert system that automatically allows the user to fit and forecast through a graphical interface. This system is equipped with a control procedure to detect trend changes and define the end of one wave and the beginning of another. Moreover, it depends on only four parameters per series that are easy to interpret and monitor along time for each variable. This feature enables us to study the effect of interventions over time in order to advise how to proceed in future outbreaks. The error correction model developed works with cointegration between series and has a great forecast capacity. Our system is prepared to work in parallel in all the Autonomous Communities of Spain. Moreover, our models are compared with a SIR model extension (SCIR) and several models of artificial intelligence.

In this work, we applied a stochastic simulation methodology to quantify the power of the detection of outlying mixture components of a stochastic model, when applying a reduced-dimension clustering technique such as Self-Organizing Maps (SOMs). The essential feature of SOMs, besides dimensional reduction into a discrete map, is the conservation of topology. In SOMs, two forms of learning are applied: competitive, by sequential allocation of sample observations to a winning node in the map, and cooperative, by the update of the weights of the winning node and its neighbors. By means of cooperative learning, the conservation of topology from the original data space to the reduced (typically 2D) map is achieved. Here, we compared the performance of one- and two-layer SOMs in the outlier representation task. The same stratified sampling was applied for both the one-layer and two-layer SOMs; although, stratification would only be relevant for the two-layer setting—to estimate the outlying mixture component detection power. Two distance measures between points in the map were defined to quantify the conservation of topology. The results of the experiment showed that the two-layer setting was more efficient in outlier detection while maintaining the basic properties of the SOM, which included adequately representing distances from the outlier component to the remaining ones.